Automated system for prioritizing investments using data-driven weighted evaluation and feature significance analysis

Abstract

An automated investment prioritization system (100) that uses data-driven weighted assessments and feature significance analyses, comprising: a data collection module configured to receive investment-related data from one or more data sources, including enterprise systems, financial databases, and external market feeds; a data processing module that is functionally coupled with the data acquisition module and configured to clean, normalize, validate, and standardize the received data; a feature extraction and analysis module that is functionally coupled with the data processing module and configured to identify a variety of evaluation features and determine the relative feature importance using statistical and machine learning techniques; a weighted valuation engine that is functionally coupled with the feature extraction and analysis module and configured to assign dynamic weights to the multitude of valuation features based on the determined feature meaning and to calculate composite valuations for a multitude of investment options; a prioritization module that is functionally coupled with the weighted valuation engine and configured to rank the multitude of investment options based on the calculated composite valuations; a visualization and reporting module configured to generate dashboards, reports, and analytical outputs that present the ranked investment options; and an adaptive feedback module that is functionally coupled with the feature extraction and analysis module and the weighted evaluation engine and is configured in such a way, that it updates feature meaning values ​​and evaluation parameters based on historical performance data and changing input conditions, wherein the system (100) is configured to provide automated, scalable and explainable prioritization of investment opportunities within enterprise environments.

Description

[0001] The present invention relates generally to the field of financial analysis, investment decision support systems, and data-driven optimization. In particular, the invention relates to an automated investment prioritization system that uses data-driven weighted assessments and feature significance analyses to evaluate and rank investment opportunities.

[0002] In modern financial and corporate environments, organizations must evaluate and prioritize numerous investment opportunities across various portfolios, including capital projects, business initiatives, and financial investments. These decisions typically require the analysis of a wide range of factors, such as expected return, risk exposure, market conditions, operational impact, and strategic alignment. Traditional investment prioritization approaches often rely on manual valuations, static valuation models, or subjective assessments, which can lead to inconsistencies, biases, and suboptimal resource allocation. Existing decision support tools offer limited automation and are generally not designed to dynamically incorporate large volumes of heterogeneous data from multiple sources.While enterprise platforms like SAP and visualization tools like Tableau facilitate data aggregation and reporting, they typically lack built-in mechanisms for automated prioritization based on advanced analytics. As a result, decision-makers often rely on fragmented workflows and siloed systems, which reduces efficiency and increases the risk of errors. Furthermore, traditional evaluation models frequently use fixed weights for evaluation criteria that may not accurately reflect changing business conditions or underlying data patterns. These static models fail to capture the relative importance of features in a data-driven manner, limiting their ability to deliver accurate and adaptive prioritization.Furthermore, the lack of transparency and explainability in many analytical approaches makes it difficult for stakeholders to understand how prioritization decisions are made. As the volume and complexity of investment-related data continue to increase, there is a growing need for systems that can automatically process, analyze, and interpret such data in real time. The absence of integrated feature significance analysis and adaptive weighting mechanisms limits the ability of existing systems to deliver meaningful insights and robust prioritization results. Accordingly, there is a need for an improved automated investment prioritization system that leverages data-driven weighted assessments and feature significance analysis to enable objective, scalable, and explainable decision-making, while seamlessly integrating with enterprise data ecosystems.

[0003] To solve this problem, the present invention provides an automated investment prioritization system that uses data-driven weighted assessments and feature significance analyses.

[0004] The system enables automated and objective prioritization of investment opportunities using data-driven weighted evaluation mechanisms.

[0005] The system reduces dependence on manual assessment and subjective judgment, thereby improving consistency and decision accuracy.

[0006] The system dynamically determines the significance of features using statistical and machine learning techniques, thus ensuring adaptive and data-driven prioritization.

[0007] The system increases transparency through comprehensible evaluation results and clear justifications for the investment rankings.

[0008] The system improves efficiency by processing large amounts of heterogeneous financial and operational data in real time.

[0009] The system supports integration with enterprise platforms such as SAP and visualization tools such as Tableau.

[0010] The system allows for flexible configuration of the evaluation criteria and thus alignment with the company's goals and strategies.

[0011] The system minimizes risks by incorporating multiple performance indicators, risk factors, and market variables into the prioritization process.

[0012] The system enables scenario analyses and sensitivity analyses for informed investment decisions.

[0013] The system improves scalability to support large and diverse investment portfolios across multiple sectors.

[0014] The system enables real-time updates of investment rankings based on changing data inputs and market conditions.

[0015] The system improves verifiability and traceability through the maintenance of detailed evaluation records and decision logs.

[0016] The present invention relates to an automated investment prioritization system that utilizes data-driven weighted assessments and feature importance analysis. The system is configured to evaluate, rank, and prioritize multiple investment opportunities by processing financial, operational, and market data within enterprise environments such as SAP and visualization platforms such as Tableau. In one aspect, the system comprises a data acquisition module configured to collect investment-related data from one or more sources, including enterprise systems, financial databases, and external market feeds, and a data processing module configured to clean, normalize, and prepare the collected data for analysis.A feature extraction and analysis module is functionally coupled with the data processing module and configured to identify relevant evaluation parameters and determine feature significance using statistical and machine learning techniques. In another aspect, the system includes a weighted evaluation engine configured to assign dynamic weights to identified features based on their relative importance and calculate composite evaluations for each investment option. A prioritization module ranks the investment options based on the calculated evaluations and generates prioritized recommendations. The system also includes a visualization and reporting module configured to present results to decision-makers via dashboards, charts, and analytical reports.Furthermore, the system includes an adaptive feedback module configured to continuously refine the weighting of feature significance and the evaluation models based on historical results, performance indicators, and changing data conditions. Accordingly, the disclosed system provides a scalable, transparent, and data-driven framework for automated investment prioritization, enabling accurate, consistent, and traceable decision-making in capital allocation.

[0017] Fig. illustrates an automated investment prioritization system using data-driven weighted evaluation and feature significance analysis.

[0018] Fig.This illustrates an automated investment prioritization system using data-driven weighted evaluation and feature significance analysis. The system includes an enterprise data source interface configured to receive transaction data, partner information, and contractual parameters from one or more enterprise systems; a data ingestion module functionally coupled to the enterprise data source interface and configured to collect and integrate data from heterogeneous sources; and a data processing module configured to clean, normalize, and validate the collected data. A revenue calculation engine is functionally coupled to the data processing module and configured to calculate partner revenue shares, commissions, and incentives based on predefined rules and contractual agreements.The system (100) further includes an AI engine that is functionally coupled with the revenue calculation engine and configured to analyze historical and real-time data to predict revenue trends, optimize partner compensation strategies, and detect revenue anomalies. A partner management module is configured to manage partner profiles, agreements, and performance metrics, while a reconciliation and validation module is configured to verify the accuracy of revenue calculations and ensure compliance with contractual and regulatory requirements.

[0019] The present invention relates to an automated investment prioritization system (100) that uses data-driven weighted assessments and feature significance analyses. The system (100) is configured to evaluate and rank multiple investment opportunities by processing heterogeneous data related to financial performance, risk metrics, operational impact, and market conditions in enterprise environments such as SAP and visualization platforms such as Tableau. In one embodiment, the system (100) includes a data acquisition module configured to receive investment-related data from multiple internal and external sources, including enterprise databases, financial systems, and market data feeds.The collected data are provided to a data processing module configured to clean, normalize, validate, and standardize the data to ensure consistency and accuracy for further analysis. In another embodiment, the system (100) includes a feature extraction and analysis module that is functionally coupled to the data processing module and configured to identify key evaluation parameters and calculate feature importance using statistical methods and machine learning techniques. A weighted evaluation engine is functionally coupled to the feature extraction module and configured to assign dynamic weights to the identified features based on their relative importance and calculate composite evaluations for each investment option.A prioritization module is configured to rank investment opportunities based on calculated assessments and generate ordered recommendations. Additionally, a visualization and reporting module is provided to present prioritization results to decision-makers via dashboards, charts, and analytical reports. The system (100) further includes an adaptive feedback module configured to continuously refine feature weights and assessment models based on historical results, user input, and evolving data patterns. In operation, the system (100) performs automated, transparent, and data-driven investment prioritization, enabling improved decision accuracy, scalability, and real-time responsiveness to changing business and market conditions.

Claims

[1] An automated investment prioritization system (100) that uses data-driven weighted assessments and feature significance analyses, comprising: a data collection module configured to receive investment-related data from one or more data sources, including enterprise systems, financial databases, and external market feeds; a data processing module that is functionally coupled with the data acquisition module and configured to clean, normalize, validate, and standardize the received data; a feature extraction and analysis module that is functionally coupled with the data processing module and configured to identify a variety of evaluation features and determine the relative feature importance using statistical and machine learning techniques; a weighted valuation engine that is functionally coupled with the feature extraction and analysis module and configured to assign dynamic weights to the multitude of valuation features based on the determined feature meaning and to calculate composite valuations for a multitude of investment options; a prioritization module that is functionally coupled with the weighted valuation engine and configured to rank the multitude of investment options based on the calculated composite valuations; a visualization and reporting module configured to generate dashboards, reports, and analytical outputs that present the ranked investment options; and an adaptive feedback module that is functionally coupled with the feature extraction and analysis module and the weighted evaluation engine and is configured in such a way, that it updates feature meaning values ​​and evaluation parameters based on historical performance data and changing input conditions, wherein the system (100) is configured to provide automated, scalable and explainable prioritization of investment opportunities within enterprise environments. [2] System (100) according to claim 1, wherein the data acquisition module is configured to integrate data from heterogeneous sources, including structured, semi-structured and unstructured datasets. [3] System (100) according to claim 1, wherein the data processing module is configured to perform data cleansing, deduplication, outlier detection and consistency checking. [4] System (100) according to claim 1, wherein the feature extraction and analysis module is configured to apply statistical correlation analyses and machine learning models to determine feature meaning. [5] System (100) according to claim 1, wherein the weighted evaluation engine is configured to dynamically adjust feature weights in response to updated feature importance values. [6] System (100) according to claim 1, wherein the prioritization module is configured to create rankings of investment options based on composite assessments. [7] System (100) according to claim 1, wherein the visualization and reporting module is configured to provide real-time dashboards, graphical representations and downloadable reports. [8] System (100) according to claim 1, wherein the adaptive feedback module is configured to refine evaluation models using historical investment results and performance indicators. [9] System (100) according to claim 1, wherein the system (100) is configured to perform scenario analyses and sensitivity analyses to evaluate the effects of changes in feature weights.

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